The efficacy of managing a construction project mainly depends on proper planning and scheduling. Mass housing projects are highly repetitive in nature, as the methods used for construction are often repetitive or cyclic. Resources are therefore being deployed repeatedly for the similar or identical tasks in these projects. In order to eradicate delays in mobilizing resources, an efficient resource schedule is required. Traditional scheduling tools like Critical Path Method (CPM), Programme Evaluation and Review Technique (PERT) are less effective in scheduling repetitive construction projects, as they consider availability of resources are unlimited. The main challenge in preparing construction schedule for repetitive projects is synchronizing the precedence logic and the allocation of resources as per requirements for all the activities. Consequently, activity scheduling and resource planning are prepared in parallel and this will facilitate in eliminating delays and idle resources across the projects thereby, controls the chain-reaction management (ripple effects). Nevertheless, almost all the repetitive scheduling methods developed so far have been giving focus on continuous repetitive projects, whereas in the present study, the emphasis is on discrete (non-linear) repetitive projects. This paper presents a model that uses genetic algorithms to optimally assign resources to repetitive activities, which aimed to minimize the total project cost & duration, idle cost & time and to maximize resource utilization. In the present study, an attempt is made to generate a resource-driven construction schedule automatically, with which resources can optimally be allocated to the activities. This schedule can be very useful in improving productivity and saving construction time and cost and also in decision-making. In addition, a case study is delineated to check the efficiency and effectiveness of the resource-driven construction schedule which is automated.